Alessandrini M, Calero Fuentes Rivera R, Falaschetti L, Pau D, Tomaselli V, Turchetti C
Department of Information Engineering, Polytechnic University of Marche, Ancona, Italy.
System Research and Applications, STMicroelectronics, Agrate Brianza, Italy.
Data Brief. 2021 Jan 29;35:106809. doi: 10.1016/j.dib.2021.106809. eCollection 2021 Apr.
Esca is one of the most common disease that can severely damage grapevine. This disease, if not properly treated in time, is the cause of vegetative stress or death of the attacked plant, with the consequence of losses in production as well as a rising risk of propagation to the closer grapevines. Nowadays, the detection of Esca is carried out manually through visual surveys usually done by agronomists, requiring enormous amount of time. Recently, image processing, computer vision and machine learning methods have been widely adopted for plant diseases classification. These methods can minimize the time spent for anomaly detection ensuring an early detection of Esca disease in grapevine plants that helps in preventing it to spread in the vineyards and in minimizing the financial loss to the wine producers. In this article, an image dataset of grapevine leaves is presented. The dataset holds grapevine leaves images belonging to two classes: unhealthy leaves acquired from plants affected by Esca disease and healthy leaves. The data presented has been collected to be used in a research project jointly developed by the Department of Information Engineering, Polytechnic University of Marche, Ancona, Italy and the STMicroelectronics, Italy, under the cooperation of the Umani Ronchi SPA winery, Osimo, Ancona, Marche, Italy. The dataset could be helpful to researchers who use machine learning and computer vision algorithms to develop applications that help agronomists in early detection of grapevine plant diseases. The dataset is freely available at http://dx.doi.org/10.17632/89cnxc58kj.1.
葡萄皮尔斯病是最常见的能严重损害葡萄树的病害之一。这种病害若不及时妥善处理，会导致受侵害植株出现营养胁迫或死亡，从而造成产量损失，并增加向附近葡萄树传播的风险。如今，葡萄皮尔斯病的检测通常由农艺师通过目视检查手动进行，这需要大量时间。最近，图像处理、计算机视觉和机器学习方法已被广泛应用于植物病害分类。这些方法可以最大限度地减少异常检测所需的时间，确保早期发现葡萄树中的皮尔斯病，有助于防止其在葡萄园传播，并将葡萄酒生产商的经济损失降至最低。本文展示了一个葡萄叶图像数据集。该数据集包含属于两类的葡萄叶图像：从受皮尔斯病影响的植株上获取的不健康叶片和健康叶片。所呈现的数据是在意大利安科纳马尔凯理工大学信息工程系、意大利意法半导体公司以及意大利安科纳马尔凯大区奥西莫的乌马尼·龙奇酒庄的合作下，为一个联合开展的研究项目收集的。该数据集可能对那些使用机器学习和计算机视觉算法开发应用程序以帮助农艺师早期检测葡萄树病害的研究人员有所帮助。该数据集可在http://dx.doi.org/10.17632/89cnxc58kj.1上免费获取。
